forked from mrq/DL-Art-School
fix mel outputs
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@ -229,10 +229,12 @@ class MusicDiffusionFid(evaluator.Evaluator):
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# 2. Decode the cheater into a MEL. This operation and the next need to be chunked to make them feasible to perform within GPU memory.
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# 2. Decode the cheater into a MEL. This operation and the next need to be chunked to make them feasible to perform within GPU memory.
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chunks = torch.split(gen_cheater, 64, dim=-1)
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chunks = torch.split(gen_cheater, 64, dim=-1)
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gen_mels = []
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gen_wavs = []
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gen_wavs = []
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for chunk in tqdm(chunks):
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for chunk in tqdm(chunks):
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gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,chunk.shape[-1]*16), progress=True,
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gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,chunk.shape[-1]*16), progress=True,
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model_kwargs={'codes': chunk.permute(0,2,1)})
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model_kwargs={'codes': chunk.permute(0,2,1)})
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gen_mels.append(gen_mel)
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# 3. And then the MEL back into a spectrogram
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# 3. And then the MEL back into a spectrogram
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output_shape = (1,16,audio.shape[-1]//(16*len(chunks)))
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output_shape = (1,16,audio.shape[-1]//(16*len(chunks)))
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@ -242,6 +244,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
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model_kwargs={'codes': gen_mel_denorm})
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model_kwargs={'codes': gen_mel_denorm})
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gen_wav = pixel_shuffle_1d(gen_wav, 16)
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gen_wav = pixel_shuffle_1d(gen_wav, 16)
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gen_wavs.append(gen_wav)
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gen_wavs.append(gen_wav)
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gen_mel = torch.cat(gen_mels, dim=-1)
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gen_wav = torch.cat(gen_wavs, dim=-1)
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gen_wav = torch.cat(gen_wavs, dim=-1)
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if audio.shape[-1] < 40 * 22050:
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if audio.shape[-1] < 40 * 22050:
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